Broken rotor bars detection in induction motors using Cubature Kalman Filter

Kowsari, Elham; Zarei, Jafar; Razavi–Far, Roozbeh; Saif, Mehrdad

doi:10.1109/iecon.2017.8217505

Cited by 8 publications

(2 citation statements)

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“…Due to the UKF shortcomings, different notions are revised and UKF systematization is offered with the aim of improving UKFs in ref [10]. UKFs are used for detecting BRB as well [11]. The centre point can be considered the most significant in sigma points and owns extra weight in performance estimation than the other ones.…”

Section: Introductionmentioning

confidence: 99%

Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor

Zaniani

Nafar

Simab

2021

IET Renewable Power Gen

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The present study tries to propose a new method which is using Central deference Kalman Filter (CDKF) as input index of deep machine learning (DML), for simulating state estimation and broken rotor bars (BRBs) diagnosis in induction motors (IMs). In addition, an advanced selective ensemble (DML) method applying the grey wolf optimization (GWO) algorithm to diagnose the BRBs in IMs is proposed in the following study. Here, in the initial step, in order to take sufficient datum within the usual efficiency, it is needed to train the DML network. The outcomes indicate that the offered scheme can more accurately and powerfully detect diverse forms of BRBs with an accuracy of more than 98%. And also, Filter precision is enhanced via changing the sigma points of the filter, however, the stability of the filter enhances more because of its utilization, and the CDKF is further stable and precise in comparison to the Unscented Kalman filter (UKF). The CDKF performance is assessed to estimate the speed and is used as input index of DML to diagnose the broken rotor bar in IMs. The obtained outcomes prove the performance of this combined scheme.

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Section: Introductionmentioning

confidence: 99%

Deep machine learning with grey wolf algorithm and central deference Kalman filter based broken rotor bars detection in induction motor

Zaniani

Nafar

Simab

2021

IET Renewable Power Gen

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“…Representam 85% do total de motores utilizados no setor e em países desenvolvidos, chegam a demandar cerca de 45% de toda energia gerada. (Martin-Diaz et al 2017) (Kowsari et al 2017) (Mirzaeva et al 2018) (Picazo-Rodenas et al 2012) Com os fatos, fica evidente a importância desta máquina, o que justifica o número de pesquisas realizadas na área. Uma grande parte dos trabalhos é voltada para a detecção de defeitos que podem ocorrer durante sua operação.…”

Section: Introductionunclassified

Parâmetros Relevantes Na Análise Termográfica De Um Motor de Indução Trifásico

Moreira

Lamim

Araújo

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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The diagnosis of engine failures is a topic that is important in the industrial context. Several techniques have been developed in this sense and one of them is the thermographic analysis. This paper presents a contribution about the interference caused by measurement parameters in a thermal analysis of an induction motor. Through different tests, the main points to be verified in a thermographic camera calibration, the errors caused by the bad adjustment of the parameters and viable solutions were presented. Resumo: O diagnóstico de falhas em motores é um tópico que tem importância no contexto industrial. Várias técnicas têm sido desenvolvidas neste sentido e uma delas é a análise termográfica. Este artigo apresenta uma contribuição acerca da interferência causada por parâmetros de medição em uma análise térmica de um motor de indução. Através de diferentes testes, foram apresentados os principais pontos a serem verificados em uma aferição por câmera termográfica, os erros causados pelo mal ajuste dos parâmetros e soluções viáveis.

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